Overview

Dataset statistics

Number of variables16
Number of observations415
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory210.4 KiB
Average record size in memory519.2 B

Variable types

Categorical3
DateTime1
Numeric8
Text4

Alerts

Country is uniformly distributedUniform
Ticker is uniformly distributedUniform

Reproduction

Analysis started2024-04-28 00:03:25.064964
Analysis finished2024-04-28 00:03:54.344450
Duration29.28 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

Country
Categorical

UNIFORM 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size30.0 KiB
united states
48 
china
47 
new zealand
47 
australia
46 
canada
46 
Other values (4)
181 

Length

Max length14
Median length11
Mean length8.9012048
Min length5

Characters and Unicode

Total characters3694
Distinct characters21
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaustralia
2nd rowaustralia
3rd rowaustralia
4th rowaustralia
5th rowaustralia

Common Values

ValueCountFrequency (%)
united states 48
11.6%
china 47
11.3%
new zealand 47
11.3%
australia 46
11.1%
canada 46
11.1%
japan 46
11.1%
united kingdom 46
11.1%
europe 45
10.8%
switzerland 44
10.6%

Length

2024-04-28T01:03:55.290028image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T01:03:55.863015image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
united 94
16.9%
states 48
8.6%
china 47
8.5%
new 47
8.5%
zealand 47
8.5%
australia 46
8.3%
canada 46
8.3%
japan 46
8.3%
kingdom 46
8.3%
europe 45
8.1%

Most occurring characters

ValueCountFrequency (%)
a 601
16.3%
n 417
11.3%
e 370
10.0%
t 280
 
7.6%
i 277
 
7.5%
d 277
 
7.5%
s 186
 
5.0%
u 185
 
5.0%
141
 
3.8%
l 137
 
3.7%
Other values (11) 823
22.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 3553
96.2%
Space Separator 141
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 601
16.9%
n 417
11.7%
e 370
10.4%
t 280
7.9%
i 277
7.8%
d 277
7.8%
s 186
 
5.2%
u 185
 
5.2%
l 137
 
3.9%
r 135
 
3.8%
Other values (10) 688
19.4%
Space Separator
ValueCountFrequency (%)
141
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3553
96.2%
Common 141
 
3.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 601
16.9%
n 417
11.7%
e 370
10.4%
t 280
7.9%
i 277
7.8%
d 277
7.8%
s 186
 
5.2%
u 185
 
5.2%
l 137
 
3.9%
r 135
 
3.8%
Other values (10) 688
19.4%
Common
ValueCountFrequency (%)
141
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3694
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 601
16.3%
n 417
11.3%
e 370
10.0%
t 280
 
7.6%
i 277
 
7.5%
d 277
 
7.5%
s 186
 
5.0%
u 185
 
5.0%
141
 
3.8%
l 137
 
3.7%
Other values (11) 823
22.3%

Date
Date

Distinct48
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size6.5 KiB
Minimum2020-01-01 00:00:00
Maximum2023-12-01 00:00:00
2024-04-28T01:03:56.601432image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:57.343720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=48)

Manufacturing PMI
Real number (ℝ)

Distinct189
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.272771
Minimum38.5
Maximum68.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-04-28T01:03:57.999041image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum38.5
5-th percentile43.556
Q148.665
median51.4
Q356.1
95-th percentile61.87
Maximum68.6
Range30.1
Interquartile range (IQR)7.435

Descriptive statistics

Standard deviation5.4723192
Coefficient of variation (CV)0.10468776
Kurtosis-0.0081125015
Mean52.272771
Median Absolute Deviation (MAD)3.6
Skewness0.33750336
Sum21693.2
Variance29.946278
MonotonicityNot monotonic
2024-04-28T01:03:58.573081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.6 12
 
2.9%
49 9
 
2.2%
50.2 7
 
1.7%
49.2 7
 
1.7%
50.1 6
 
1.4%
47.8 5
 
1.2%
48.8 5
 
1.2%
50.6 5
 
1.2%
47.7 5
 
1.2%
48.7 5
 
1.2%
Other values (179) 349
84.1%
ValueCountFrequency (%)
38.5 1
0.2%
38.9 1
0.2%
39.2 1
0.2%
39.9 1
0.2%
40.1 1
0.2%
40.6 1
0.2%
40.9 1
0.2%
41.4 1
0.2%
41.7 1
0.2%
42.1 1
0.2%
ValueCountFrequency (%)
68.6 1
0.2%
67.6 1
0.2%
67.1 1
0.2%
67 1
0.2%
66.3 1
0.2%
65.7 1
0.2%
65.6 1
0.2%
64.7 1
0.2%
64.4 1
0.2%
64.2 1
0.2%

Services PMI
Real number (ℝ)

Distinct202
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.049759
Minimum33.8
Maximum67.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-04-28T01:03:59.021362image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum33.8
5-th percentile44.41
Q148.84
median52.1
Q355.2
95-th percentile60.281
Maximum67.6
Range33.8
Interquartile range (IQR)6.36

Descriptive statistics

Standard deviation5.0161351
Coefficient of variation (CV)0.096371918
Kurtosis1.182898
Mean52.049759
Median Absolute Deviation (MAD)3.16
Skewness-0.29301366
Sum21600.65
Variance25.161612
MonotonicityNot monotonic
2024-04-28T01:03:59.454450image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
49.5 7
 
1.7%
50.3 7
 
1.7%
55.2 6
 
1.4%
53.2 6
 
1.4%
56.4 6
 
1.4%
56.1 6
 
1.4%
47.8 6
 
1.4%
50.7 6
 
1.4%
52.6 6
 
1.4%
55.9 5
 
1.2%
Other values (192) 354
85.3%
ValueCountFrequency (%)
33.8 1
0.2%
33.9 1
0.2%
34.5 1
0.2%
36.82 1
0.2%
37.8 1
0.2%
38 1
0.2%
38.3 1
0.2%
38.5 1
0.2%
39.5 1
0.2%
41.6 1
0.2%
ValueCountFrequency (%)
67.6 1
0.2%
66.6 1
0.2%
64 1
0.2%
63.9 1
0.2%
63.6 1
0.2%
63 1
0.2%
62.9 1
0.2%
62.6 1
0.2%
62.4 1
0.2%
62.3 1
0.2%

Consumer Confidence
Real number (ℝ)

Distinct310
Distinct (%)74.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.638795
Minimum-52.5
Maximum127
Zeros0
Zeros (%)0.0%
Negative130
Negative (%)31.3%
Memory size6.5 KiB
2024-04-28T01:03:59.940609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-52.5
5-th percentile-39.63
Q1-9.65
median50
Q387
95-th percentile119.59
Maximum127
Range179.5
Interquartile range (IQR)96.65

Descriptive statistics

Standard deviation51.775012
Coefficient of variation (CV)1.1864446
Kurtosis-1.2592181
Mean43.638795
Median Absolute Deviation (MAD)45.1
Skewness-0.22588353
Sum18110.1
Variance2680.6519
MonotonicityNot monotonic
2024-04-28T01:04:00.553657image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-14 4
 
1.0%
87.6 4
 
1.0%
88.9 4
 
1.0%
-30 4
 
1.0%
-9 4
 
1.0%
92.1 3
 
0.7%
105.2 3
 
0.7%
107.1 3
 
0.7%
102.7 3
 
0.7%
99.1 3
 
0.7%
Other values (300) 380
91.6%
ValueCountFrequency (%)
-52.5 1
 
0.2%
-50.8 1
 
0.2%
-49 1
 
0.2%
-48 1
 
0.2%
-47 1
 
0.2%
-46.6 3
0.7%
-45 1
 
0.2%
-44.4 1
 
0.2%
-44 2
0.5%
-42 1
 
0.2%
ValueCountFrequency (%)
127 1
 
0.2%
126.4 1
 
0.2%
124 1
 
0.2%
122.8 3
0.7%
122.2 2
0.5%
122.1 1
 
0.2%
121.8 2
0.5%
121.7 1
 
0.2%
121.5 3
0.7%
121.2 2
0.5%

Interest Rates
Categorical

Distinct46
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
0.25%
73 
-0.10%
46 
0.10%
37 
-0.75%
27 
0.00%
26 
Other values (41)
206 

Length

Max length6
Median length5
Mean length5.1831325
Min length5

Characters and Unicode

Total characters2151
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)2.2%

Sample

1st row0.75%
2nd row0.75%
3rd row0.50%
4th row0.25%
5th row0.25%

Common Values

ValueCountFrequency (%)
0.25% 73
17.6%
-0.10% 46
 
11.1%
0.10% 37
 
8.9%
-0.75% 27
 
6.5%
0.00% 26
 
6.3%
3.85% 17
 
4.1%
5.50% 14
 
3.4%
4.50% 12
 
2.9%
1.00% 12
 
2.9%
0.50% 11
 
2.7%
Other values (36) 140
33.7%

Length

2024-04-28T01:04:01.094375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0.10 83
20.0%
0.25 76
18.3%
0.75 35
 
8.4%
0.00 26
 
6.3%
3.85 17
 
4.1%
5.50 14
 
3.4%
4.50 12
 
2.9%
1.00 12
 
2.9%
0.50 11
 
2.7%
1.75 11
 
2.7%
Other values (33) 118
28.4%

Most occurring characters

ValueCountFrequency (%)
0 504
23.4%
. 415
19.3%
% 415
19.3%
5 285
13.2%
1 126
 
5.9%
2 118
 
5.5%
- 76
 
3.5%
3 72
 
3.3%
7 62
 
2.9%
4 41
 
1.9%
Other values (2) 37
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1245
57.9%
Other Punctuation 830
38.6%
Dash Punctuation 76
 
3.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 504
40.5%
5 285
22.9%
1 126
 
10.1%
2 118
 
9.5%
3 72
 
5.8%
7 62
 
5.0%
4 41
 
3.3%
8 24
 
1.9%
6 13
 
1.0%
Other Punctuation
ValueCountFrequency (%)
. 415
50.0%
% 415
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 76
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2151
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 504
23.4%
. 415
19.3%
% 415
19.3%
5 285
13.2%
1 126
 
5.9%
2 118
 
5.5%
- 76
 
3.5%
3 72
 
3.3%
7 62
 
2.9%
4 41
 
1.9%
Other values (2) 37
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 504
23.4%
. 415
19.3%
% 415
19.3%
5 285
13.2%
1 126
 
5.9%
2 118
 
5.5%
- 76
 
3.5%
3 72
 
3.3%
7 62
 
2.9%
4 41
 
1.9%
Other values (2) 37
 
1.7%

CPI YOY
Text

Distinct107
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-04-28T01:04:01.842079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.1108434
Min length5

Characters and Unicode

Total characters2121
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)6.5%

Sample

1st row2.20%
2nd row2.20%
3rd row2.20%
4th row-0.30%
5th row0.70%
ValueCountFrequency (%)
0.90 15
 
3.6%
0.70 14
 
3.4%
1.50 13
 
3.1%
0.30 13
 
3.1%
1.40 11
 
2.7%
3.30 11
 
2.7%
2.50 11
 
2.7%
0.20 10
 
2.4%
3.00 10
 
2.4%
0.50 10
 
2.4%
Other values (85) 297
71.6%
2024-04-28T01:04:02.937765image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 560
26.4%
. 415
19.6%
% 415
19.6%
1 124
 
5.8%
3 103
 
4.9%
2 91
 
4.3%
5 79
 
3.7%
4 77
 
3.6%
7 75
 
3.5%
6 58
 
2.7%
Other values (3) 124
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1255
59.2%
Other Punctuation 830
39.1%
Dash Punctuation 36
 
1.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 560
44.6%
1 124
 
9.9%
3 103
 
8.2%
2 91
 
7.3%
5 79
 
6.3%
4 77
 
6.1%
7 75
 
6.0%
6 58
 
4.6%
9 50
 
4.0%
8 38
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 415
50.0%
% 415
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2121
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 560
26.4%
. 415
19.6%
% 415
19.6%
1 124
 
5.8%
3 103
 
4.9%
2 91
 
4.3%
5 79
 
3.7%
4 77
 
3.6%
7 75
 
3.5%
6 58
 
2.7%
Other values (3) 124
 
5.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2121
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 560
26.4%
. 415
19.6%
% 415
19.6%
1 124
 
5.8%
3 103
 
4.9%
2 91
 
4.3%
5 79
 
3.7%
4 77
 
3.6%
7 75
 
3.5%
6 58
 
2.7%
Other values (3) 124
 
5.8%

Core CPI
Text

Distinct83
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-04-28T01:04:03.762666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0578313
Min length5

Characters and Unicode

Total characters2099
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)1.7%

Sample

1st row1.70%
2nd row1.70%
3rd row1.70%
4th row1.20%
5th row1.10%
ValueCountFrequency (%)
1.10 18
 
4.3%
0.60 13
 
3.1%
1.60 13
 
3.1%
1.20 12
 
2.9%
0.80 12
 
2.9%
1.70 12
 
2.9%
0.90 11
 
2.7%
6.20 10
 
2.4%
0.70 10
 
2.4%
0.20 10
 
2.4%
Other values (63) 294
70.8%
2024-04-28T01:04:05.225492image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 549
26.2%
. 415
19.8%
% 415
19.8%
1 143
 
6.8%
2 108
 
5.1%
6 89
 
4.2%
5 80
 
3.8%
4 78
 
3.7%
3 71
 
3.4%
7 52
 
2.5%
Other values (3) 99
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1245
59.3%
Other Punctuation 830
39.5%
Dash Punctuation 24
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 549
44.1%
1 143
 
11.5%
2 108
 
8.7%
6 89
 
7.1%
5 80
 
6.4%
4 78
 
6.3%
3 71
 
5.7%
7 52
 
4.2%
8 38
 
3.1%
9 37
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 415
50.0%
% 415
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2099
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 549
26.2%
. 415
19.8%
% 415
19.8%
1 143
 
6.8%
2 108
 
5.1%
6 89
 
4.2%
5 80
 
3.8%
4 78
 
3.7%
3 71
 
3.4%
7 52
 
2.5%
Other values (3) 99
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2099
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 549
26.2%
. 415
19.8%
% 415
19.8%
1 143
 
6.8%
2 108
 
5.1%
6 89
 
4.2%
5 80
 
3.8%
4 78
 
3.7%
3 71
 
3.4%
7 52
 
2.5%
Other values (3) 99
 
4.7%

Unemployment Rate
Text

Distinct75
Distinct (%)18.1%
Missing0
Missing (%)0.0%
Memory size28.4 KiB
2024-04-28T01:04:06.039558image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0168675
Min length5

Characters and Unicode

Total characters2082
Distinct characters12
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)4.6%

Sample

1st row5.20%
2nd row5.10%
3rd row5.20%
4th row7.40%
5th row7.50%
ValueCountFrequency (%)
3.60 22
 
5.3%
4.00 18
 
4.3%
3.90 16
 
3.9%
5.00 15
 
3.6%
2.60 15
 
3.6%
5.20 15
 
3.6%
5.30 14
 
3.4%
3.40 13
 
3.1%
5.50 13
 
3.1%
2.50 12
 
2.9%
Other values (65) 262
63.1%
2024-04-28T01:04:08.695788image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 472
22.7%
. 415
19.9%
% 415
19.9%
5 147
 
7.1%
3 136
 
6.5%
2 115
 
5.5%
4 94
 
4.5%
6 91
 
4.4%
7 56
 
2.7%
8 53
 
2.5%
Other values (2) 88
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1252
60.1%
Other Punctuation 830
39.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 472
37.7%
5 147
 
11.7%
3 136
 
10.9%
2 115
 
9.2%
4 94
 
7.5%
6 91
 
7.3%
7 56
 
4.5%
8 53
 
4.2%
9 45
 
3.6%
1 43
 
3.4%
Other Punctuation
ValueCountFrequency (%)
. 415
50.0%
% 415
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2082
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 472
22.7%
. 415
19.9%
% 415
19.9%
5 147
 
7.1%
3 136
 
6.5%
2 115
 
5.5%
4 94
 
4.5%
6 91
 
4.4%
7 56
 
2.7%
8 53
 
2.5%
Other values (2) 88
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2082
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 472
22.7%
. 415
19.9%
% 415
19.9%
5 147
 
7.1%
3 136
 
6.5%
2 115
 
5.5%
4 94
 
4.5%
6 91
 
4.4%
7 56
 
2.7%
8 53
 
2.5%
Other values (2) 88
 
4.2%

GDP YOY
Text

Distinct88
Distinct (%)21.2%
Missing0
Missing (%)0.0%
Memory size28.5 KiB
2024-04-28T01:04:09.339082image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2771084
Min length5

Characters and Unicode

Total characters2190
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)1.2%

Sample

1st row1.20%
2nd row1.20%
3rd row1.20%
4th row-6.10%
5th row-3.20%
ValueCountFrequency (%)
0.60 21
 
5.1%
0.30 18
 
4.3%
0.40 17
 
4.1%
1.50 15
 
3.6%
1.70 12
 
2.9%
4.90 12
 
2.9%
3.90 12
 
2.9%
0.20 12
 
2.9%
2.10 12
 
2.9%
4.50 9
 
2.2%
Other values (62) 275
66.3%
2024-04-28T01:04:10.242356image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 556
25.4%
. 415
18.9%
% 415
18.9%
1 145
 
6.6%
2 112
 
5.1%
4 102
 
4.7%
5 88
 
4.0%
- 86
 
3.9%
3 71
 
3.2%
6 66
 
3.0%
Other values (3) 134
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1274
58.2%
Other Punctuation 830
37.9%
Dash Punctuation 86
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 556
43.6%
1 145
 
11.4%
2 112
 
8.8%
4 102
 
8.0%
5 88
 
6.9%
3 71
 
5.6%
6 66
 
5.2%
9 63
 
4.9%
7 45
 
3.5%
8 26
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 415
50.0%
% 415
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2190
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 556
25.4%
. 415
18.9%
% 415
18.9%
1 145
 
6.6%
2 112
 
5.1%
4 102
 
4.7%
5 88
 
4.0%
- 86
 
3.9%
3 71
 
3.2%
6 66
 
3.0%
Other values (3) 134
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2190
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 556
25.4%
. 415
18.9%
% 415
18.9%
1 145
 
6.6%
2 112
 
5.1%
4 102
 
4.7%
5 88
 
4.0%
- 86
 
3.9%
3 71
 
3.2%
6 66
 
3.0%
Other values (3) 134
 
6.1%

Ticker
Categorical

UNIFORM 

Distinct9
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size28.6 KiB
dxy
48 
usdcny
47 
nzdusd
47 
audusd
46 
usdcad
46 
Other values (4)
181 

Length

Max length6
Median length6
Mean length5.653012
Min length3

Characters and Unicode

Total characters2346
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowaudusd
2nd rowaudusd
3rd rowaudusd
4th rowaudusd
5th rowaudusd

Common Values

ValueCountFrequency (%)
dxy 48
11.6%
usdcny 47
11.3%
nzdusd 47
11.3%
audusd 46
11.1%
usdcad 46
11.1%
usdjpy 46
11.1%
gbpusd 46
11.1%
eurusd 45
10.8%
usdchf 44
10.6%

Length

2024-04-28T01:04:10.780718image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-28T01:04:11.244476image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
dxy 48
11.6%
usdcny 47
11.3%
nzdusd 47
11.3%
audusd 46
11.1%
usdcad 46
11.1%
usdjpy 46
11.1%
gbpusd 46
11.1%
eurusd 45
10.8%
usdchf 44
10.6%

Most occurring characters

ValueCountFrequency (%)
d 554
23.6%
u 458
19.5%
s 367
15.6%
y 141
 
6.0%
c 137
 
5.8%
n 94
 
4.0%
p 92
 
3.9%
a 92
 
3.9%
x 48
 
2.0%
z 47
 
2.0%
Other values (7) 316
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2346
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
d 554
23.6%
u 458
19.5%
s 367
15.6%
y 141
 
6.0%
c 137
 
5.8%
n 94
 
4.0%
p 92
 
3.9%
a 92
 
3.9%
x 48
 
2.0%
z 47
 
2.0%
Other values (7) 316
13.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 2346
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
d 554
23.6%
u 458
19.5%
s 367
15.6%
y 141
 
6.0%
c 137
 
5.8%
n 94
 
4.0%
p 92
 
3.9%
a 92
 
3.9%
x 48
 
2.0%
z 47
 
2.0%
Other values (7) 316
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
d 554
23.6%
u 458
19.5%
s 367
15.6%
y 141
 
6.0%
c 137
 
5.8%
n 94
 
4.0%
p 92
 
3.9%
a 92
 
3.9%
x 48
 
2.0%
z 47
 
2.0%
Other values (7) 316
13.5%

Open
Real number (ℝ)

Distinct411
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.428119
Minimum0.5593
Maximum151.71
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-04-28T01:04:11.717747image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.5593
5-th percentile0.63843
Q10.9093
median1.2622
Q37.0934
95-th percentile117.092
Maximum151.71
Range151.1507
Interquartile range (IQR)6.1841

Descriptive statistics

Standard deviation46.20978
Coefficient of variation (CV)1.7485081
Kurtosis0.24018839
Mean26.428119
Median Absolute Deviation (MAD)0.555
Skewness1.4216667
Sum10967.67
Variance2135.3437
MonotonicityNot monotonic
2024-04-28T01:04:12.378649image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.669 2
 
0.5%
0.6903 2
 
0.5%
1.3572 2
 
0.5%
1.2679 2
 
0.5%
0.7021 1
 
0.2%
0.9681 1
 
0.2%
0.9089 1
 
0.2%
0.9157 1
 
0.2%
0.9209 1
 
0.2%
0.9036 1
 
0.2%
Other values (401) 401
96.6%
ValueCountFrequency (%)
0.5593 1
0.2%
0.5817 1
0.2%
0.5824 1
0.2%
0.5967 1
0.2%
0.6003 1
0.2%
0.6023 1
0.2%
0.612 1
0.2%
0.6121 1
0.2%
0.6126 1
0.2%
0.6154 1
0.2%
ValueCountFrequency (%)
151.71 1
0.2%
149.48 1
0.2%
148.71 1
0.2%
148.21 1
0.2%
145.54 1
0.2%
144.61 1
0.2%
144.37 1
0.2%
142.3 1
0.2%
139.35 1
0.2%
138.97 1
0.2%

High
Real number (ℝ)

Distinct411
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.975902
Minimum0.5874
Maximum151.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-04-28T01:04:13.426071image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.5874
5-th percentile0.6529
Q10.9203
median1.2838
Q37.18415
95-th percentile126.955
Maximum151.96
Range151.3726
Interquartile range (IQR)6.26385

Descriptive statistics

Standard deviation47.226366
Coefficient of variation (CV)1.7506872
Kurtosis0.26456425
Mean26.975902
Median Absolute Deviation (MAD)0.556
Skewness1.4275811
Sum11194.999
Variance2230.3296
MonotonicityNot monotonic
2024-04-28T01:04:14.329799image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.191 3
 
0.7%
1.1096 2
 
0.5%
0.6725 2
 
0.5%
0.7031 1
 
0.2%
0.9219 1
 
0.2%
0.9299 1
 
0.2%
0.9241 1
 
0.2%
0.9495 1
 
0.2%
0.9651 1
 
0.2%
0.9785 1
 
0.2%
Other values (401) 401
96.6%
ValueCountFrequency (%)
0.5874 1
0.2%
0.6051 1
0.2%
0.6057 1
0.2%
0.6162 1
0.2%
0.6209 1
0.2%
0.622 1
0.2%
0.6241 1
0.2%
0.6249 1
0.2%
0.6308 1
0.2%
0.6314 1
0.2%
ValueCountFrequency (%)
151.96 1
0.2%
151.95 1
0.2%
151.76 1
0.2%
149.74 1
0.2%
148.83 1
0.2%
148.35 1
0.2%
147.38 1
0.2%
145.91 1
0.2%
145.08 1
0.2%
144.96 1
0.2%

Low
Real number (ℝ)

Distinct403
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.975701
Minimum0.547
Maximum147.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-04-28T01:04:14.944516image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.547
5-th percentile0.6188
Q10.88945
median1.2363
Q37.0393
95-th percentile116.749
Maximum147.38
Range146.833
Interquartile range (IQR)6.14985

Descriptive statistics

Standard deviation45.365177
Coefficient of variation (CV)1.7464467
Kurtosis0.20034042
Mean25.975701
Median Absolute Deviation (MAD)0.5421
Skewness1.4133664
Sum10779.916
Variance2057.9992
MonotonicityNot monotonic
2024-04-28T01:04:15.637652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3314 2
 
0.5%
0.6191 2
 
0.5%
0.915 2
 
0.5%
1.1185 2
 
0.5%
0.9071 2
 
0.5%
0.7563 2
 
0.5%
101.92 2
 
0.5%
0.9102 2
 
0.5%
0.6876 2
 
0.5%
0.7002 2
 
0.5%
Other values (393) 395
95.2%
ValueCountFrequency (%)
0.547 1
0.2%
0.5507 1
0.2%
0.5512 1
0.2%
0.5565 1
0.2%
0.5739 1
0.2%
0.5773 1
0.2%
0.5788 1
0.2%
0.5859 1
0.2%
0.5885 1
0.2%
0.592 1
0.2%
ValueCountFrequency (%)
147.38 1
0.2%
146.66 1
0.2%
144.44 1
0.2%
143.52 1
0.2%
141.52 1
0.2%
140.25 1
0.2%
138.92 1
0.2%
138.46 1
0.2%
137.49 1
0.2%
137.24 1
0.2%

Close
Real number (ℝ)

Distinct411
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.517403
Minimum0.5594
Maximum151.67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.5 KiB
2024-04-28T01:04:16.109025image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.5594
5-th percentile0.63321
Q10.9086
median1.2614
Q37.10435
95-th percentile123.766
Maximum151.67
Range151.1106
Interquartile range (IQR)6.19575

Descriptive statistics

Standard deviation46.393426
Coefficient of variation (CV)1.7495464
Kurtosis0.24774193
Mean26.517403
Median Absolute Deviation (MAD)0.555
Skewness1.4237177
Sum11004.722
Variance2152.35
MonotonicityNot monotonic
2024-04-28T01:04:16.774620image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
104.68 2
 
0.5%
6.473 2
 
0.5%
97.39 2
 
0.5%
1.0576 2
 
0.5%
0.6691 1
 
0.2%
0.9127 1
 
0.2%
0.9471 1
 
0.2%
0.9612 1
 
0.2%
0.9653 1
 
0.2%
0.963 1
 
0.2%
Other values (401) 401
96.6%
ValueCountFrequency (%)
0.5594 1
0.2%
0.5813 1
0.2%
0.5825 1
0.2%
0.5954 1
0.2%
0.5964 1
0.2%
0.5995 1
0.2%
0.6021 1
0.2%
0.6119 1
0.2%
0.6124 1
0.2%
0.6135 1
0.2%
ValueCountFrequency (%)
151.67 1
0.2%
149.35 1
0.2%
148.71 1
0.2%
148.19 1
0.2%
145.53 1
0.2%
144.75 1
0.2%
144.32 1
0.2%
142.28 1
0.2%
141.06 1
0.2%
139.34 1
0.2%

z_score
Real number (ℝ)

Distinct202
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.10298774
Minimum-2.7305585
Maximum2.5173937
Zeros0
Zeros (%)0.0%
Negative184
Negative (%)44.3%
Memory size6.5 KiB
2024-04-28T01:04:17.516502image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-2.7305585
5-th percentile-1.0831983
Q1-0.39537503
median0.1107884
Q30.59210945
95-th percentile1.3810102
Maximum2.5173937
Range5.2479521
Interquartile range (IQR)0.98748448

Descriptive statistics

Standard deviation0.7788295
Coefficient of variation (CV)7.5623513
Kurtosis1.182898
Mean0.10298774
Median Absolute Deviation (MAD)0.49063695
Skewness-0.29301366
Sum42.739914
Variance0.60657539
MonotonicityNot monotonic
2024-04-28T01:04:17.929120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.2929002259 7
 
1.7%
-0.1686883411 7
 
1.7%
0.5921094534 6
 
1.4%
0.2815797413 6
 
1.4%
0.7784272806 6
 
1.4%
0.7318478238 6
 
1.4%
-0.5568504811 6
 
1.4%
-0.1065823987 6
 
1.4%
0.1884208277 6
 
1.4%
0.7007948526 5
 
1.2%
Other values (192) 354
85.3%
ValueCountFrequency (%)
-2.730558465 1
0.2%
-2.71503198 1
0.2%
-2.621873066 1
0.2%
-2.2616586 1
0.2%
-2.109499041 1
0.2%
-2.07844607 1
0.2%
-2.031866613 1
0.2%
-2.000813642 1
0.2%
-1.845548786 1
0.2%
-1.519492588 1
0.2%
ValueCountFrequency (%)
2.517393668 1
0.2%
2.362128812 1
0.2%
1.958440186 1
0.2%
1.942913701 1
0.2%
1.896334244 1
0.2%
1.80317533 1
0.2%
1.787648845 1
0.2%
1.741069388 1
0.2%
1.710016417 1
0.2%
1.694489931 1
0.2%

Interactions

2024-04-28T01:03:50.006360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:27.549241image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:31.005441image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:34.661606image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:37.853321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:40.793861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:44.126693image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:47.054973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:50.391484image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:28.083466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:31.348734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:35.098769image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:38.261626image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:41.157559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:44.568229image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:47.462027image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:50.706240image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:28.381683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:31.673553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:35.455227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:38.622380image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:41.595272image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:44.922249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:47.764129image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:51.026407image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:28.763038image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:32.125175image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:35.908131image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:38.996889image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:42.032737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:45.294493image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:48.136415image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:51.305522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:29.109986image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:32.558745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:36.337773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:39.369163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:42.492858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:45.617890image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:48.515526image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:51.682299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:29.710581image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:32.981307image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:36.690144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:39.711201image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:42.885996image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:45.974430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:48.929746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:52.095775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:30.131210image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:33.359335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:37.084758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:40.063534image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:43.265087image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:46.321631image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:49.281461image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:52.421269image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:30.614682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:33.995525image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:37.478599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:40.497748image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:43.729994image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:46.683843image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-04-28T01:03:49.697004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-04-28T01:03:53.048609image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-28T01:03:54.030981image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

CountryDateManufacturing PMIServices PMIConsumer ConfidenceInterest RatesCPI YOYCore CPIUnemployment RateGDP YOYTickerOpenHighLowClosez_score
0australia2020-01-0149.650.693.40.75%2.20%1.70%5.20%1.20%audusd0.70210.70310.66820.6691-0.122109
1australia2020-02-0150.249.095.50.75%2.20%1.70%5.10%1.20%audusd0.66900.67760.64340.6509-0.370533
2australia2020-03-0149.738.591.90.50%2.20%1.70%5.20%1.20%audusd0.64880.66860.55070.6135-2.000814
5australia2020-06-0151.253.193.70.25%-0.30%1.20%7.40%-6.10%audusd0.66570.70630.66460.69020.266053
6australia2020-07-0154.058.287.90.25%0.70%1.10%7.50%-3.20%audusd0.69030.72290.68770.71421.057904
7australia2020-08-0153.649.079.50.25%0.70%1.10%6.80%-3.20%audusd0.71460.74040.70750.7375-0.370533
8australia2020-09-0155.450.893.90.25%0.70%1.10%6.90%-3.20%audusd0.73770.74150.70060.7161-0.091056
9australia2020-10-0154.253.7105.00.25%0.90%1.10%7.00%-0.40%audusd0.71630.72440.70020.70260.359212
10australia2020-11-0155.855.1107.70.10%0.90%1.10%6.80%-0.40%audusd0.70340.74080.69910.73450.576583
11australia2020-12-0155.757.0112.00.10%0.90%1.10%6.50%-0.40%audusd0.73450.77430.73400.76940.871586
CountryDateManufacturing PMIServices PMIConsumer ConfidenceInterest RatesCPI YOYCore CPIUnemployment RateGDP YOYTickerOpenHighLowClosez_score
422united states2023-03-0146.351.262.04.75%5.00%5.60%3.50%1.70%dxy105.04105.88101.92102.51-0.028950
423united states2023-04-0147.151.963.55.00%4.90%5.50%3.40%2.40%dxy102.59103.06100.79101.660.079735
424united states2023-05-0146.950.359.25.00%4.00%5.30%3.70%2.40%dxy101.67104.70101.03104.33-0.168688
425united states2023-06-0146.053.963.95.25%3.00%4.80%3.60%2.40%dxy104.15104.50101.92102.910.390265
426united states2023-07-0146.452.772.65.25%3.20%4.70%3.50%2.90%dxy102.93103.5799.58101.860.203947
427united states2023-08-0147.654.571.25.50%3.70%4.30%3.80%2.90%dxy101.87104.45101.74103.620.483424
428united states2023-09-0149.053.667.75.50%3.70%4.10%3.80%2.90%dxy103.62106.84103.27106.220.343686
429united states2023-10-0146.751.863.85.50%3.20%4.00%3.90%3.10%dxy106.17107.35105.36106.660.064209
430united states2023-11-0146.752.561.35.50%3.10%4.00%3.70%3.10%dxy106.67107.11102.47103.500.172894
431united states2023-12-0147.450.569.75.50%3.40%3.90%3.70%3.10%dxy103.36104.26100.62101.33-0.137635